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All Journal EKSAKTA : Jurnal Penelitian dan Pembelajaran MIPA Pendas : Jurnah Ilmiah Pendidikan Dasar Journal on Education KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Antivirus : Jurnal Ilmiah Teknik Informatika Jurnal ABDINUS : Jurnal Pengabdian Nusantara Journal of Intelligent Decision Support System (IDSS) Jurnal Bumigora Information Technology (BITe) Jurnal Mandiri IT Smart City Enrichment: Journal of Multidisciplinary Research and Development VISA: Journal of Vision and Ideas Asian Journal of Social and Humanities SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan
H. A. Danang Rimbawa
Universitas Pertahanan Republik Indonesia
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Other

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Redefining hash functions for quantum security with SHA 256 Riswantoro, Dadan Shavkat; Rimbawa, H.A Danang
Journal of Intelligent Decision Support System (IDSS) Vol 8 No 2 (2025): June: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v8i2.301

Abstract

The rapid advancement of quantum computing technology presents a significant challenge to the field of cryptography, particularly affecting the security of hash functions that form the foundation of many cryptographic protocols. Hash functions are widely used to ensure data integrity, generate digital signatures, and securely store passwords. However, the emergence of quantum algorithms—such as Grover’s algorithm—threatens to undermine the security assumptions on which these hash functions are based by significantly reducing their effective security levels.  This paper aims to provide a comprehensive analysis of the vulnerabilities introduced by quantum computing to traditional hash functions, detailing how these weaknesses can be exploited by quantum adversaries. We explore the fundamental properties of hash functions, including pre-image resistance, second pre-image resistance, and collision resistance, and assess how these properties are affected in a quantum context. Furthermore, we examine the implications of these vulnerabilities for existing cryptographic systems and emphasize the urgent need for the development of post-quantum cryptographic standards. In response to these challenges, we review ongoing research efforts focused on designing hash functions that are resilient to quantum attacks. We evaluate several promising candidates for post-quantum hash functions, considering their security properties, performance metrics, and practical applicability. The findings of this paper highlight the necessity of transitioning to post-quantum cryptographic solutions to safeguard sensitive information in an increasingly quantum-capable world. Ultimately, we advocate for proactive measures within the cryptographic community to adopt and implement these new standards, thereby ensuring robust data security in the age of quantum computing.
Studi Pustaka: Optimalisasi Deteksi Malware melalui Integrasi Pembelajaran Mesin Heuristik dan Big Data untuk Keamanan Siber Supriyadi, Devi; Wahyudi, Bisyron; Rimbawa, Danang
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 14 No 1 (2025): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v14i1.15595

Abstract

The increasingly complex and dynamic threat of malware drives the need for a more adaptive detection strategy than conventional signature-based methods. This study aims to evaluate the effectiveness of machine learning, heuristics, and big data approaches in detecting modern malware. The main problem raised is the limitation of traditional methods in identifying new malware variants, especially those that use obfuscation techniques such as polymorphism and metamorphism. Using a systematic literature study approach to the 2016-2024 literature from various reputable sources, this study compares the performance of each approach based on accuracy, efficiency, and resistance to adversarial attacks. The results of the analysis show that deep learning models such as the Convolutional Neural Network (CNN) have the highest detection accuracy, while heuristic methods excel in initial detection efficiency, and big data provides advantages in the scalability of real-time detection systems. This study concludes that the hybrid integration of these three approaches has the potential to create a malware detection system that is more adaptive and resilient to cyberattacks, although further empirical validation is still needed for real-world implementation.
Artificial intelligence-based hand gesture recognition for sign language interpretation Rais, M. Fazil; AlFatrah, M. Ilham; Noorta, Chadafa Zulti; Rimbawa, H.A Danang; Atturoybi, Abdurrosyid
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.395

Abstract

This paper presents an artificial intelligence-based system for real-time hand gesture recognition to support sign language interpretation for the deaf and hard-of-hearing community. The proposed system integrates computer vision techniques with deep learning models to accurately identify static hand gestures representing alphabetic signs. The MediaPipe framework is employed to detect and track hand landmarks from live video input, which are then processed and classified using a Convolutional Neural Network (CNN) model. The model is trained on a publicly available BISINDO (Bahasa Isyarat Indonesia) gesture dataset retrieved from Kaggle, comprising 312 images across 26 hand gestures captured under multiple background conditions. Preprocessing includes resizing, grayscale conversion, data augmentation, and landmark extraction with specific innovations in preprocessing techniques, such as the use of advanced data augmentation methods and landmark normalization, which significantly enhance gesture identification accuracy and model robustness. Experimental results show that the system achieves an average classification accuracy of 88.03% and maintains stable performance in real-time applications. Despite these promising results, the system exhibits limitations, including challenges with dynamic gesture recognition, background interference, and limited handling of complex hand movements, all of which can be explored in future research to improve the system’s accuracy and generalization. These findings highlight the system’s potential as an inclusive communication tool to bridge language barriers between deaf individuals and non-signers. This research contributes to the development of accessible assistive technologies by demonstrating a non-intrusive, vision-based approach to sign language interpretation. Future development may involve dynamic gesture translation, sentence-level recognition, and deployment on mobile platforms.
Determination of desalination system pricing in Penjaringan Utara subdistrict, North Jakarta using HOMER simulation Rimbawa, H. A. Danang; Nurrahman, Muhammad Irsyaad; Saragih, Gabriel Winandika
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.399

Abstract

The coastal area of Muara Angke in North Jakarta grapples with significant challenges regarding access to clean water, compounded by limited energy availability. In 2024, a desalination project utilizing reverse osmosis (RO) technology was launched to address this issue, with a capacity to produce 320 liters of clean water per hour. A key feature of this initiative is its reliance on solar energy, which offers a sustainable solution to the region’s energy constraints. However, to ensure the project's long-term viability, it is essential to conduct an economic evaluation, particularly focusing on the cost of producing the desalinated water. This cost is intricately linked to the energy required to power the reverse osmosis (RO) system. Specifically, the Cost of Energy (CoE) plays a crucial role in determining the price of the desalinated water. To assess the economic feasibility of the solar-powered desalination system, HOMER simulation software was used to model the performance of the solar power system, considering factors such as solar energy potential, system capacity, and the financial costs of the solar infrastructure, including the solar panels, inverters, and battery storage. The simulation results reveal an annual energy production of 58,900 kWh and a CoE of Rp 575.55 per kWh, which directly influences the cost of water production, resulting in a price of Rp 17.27 per liter for the desalinated water. This study highlights the essential role of renewable energy sources in ensuring the sustainability of desalination systems and emphasizes the importance of accurate cost analysis to make such systems economically viable in coastal communities like Muara Angke. By integrating RO technology with solar energy, this initiative offers a promising approach to addressing water scarcity while reducing reliance on non-renewable energy sources, ultimately providing a long-term solution to clean water access.
Machine learning-based approach for evaluating physical fitness through motion detection Rais, M. Fazil; Chadafa Zulti Noorta; M. Ilham AlFatrah; H.A Danang Rimbawa; Fatmawati, Uvi Desi
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.406

Abstract

Physical fitness assessment is crucial for evaluating an individual's physical performance and endurance. However, traditional methods often rely on manual observation, which can lead to subjectivity and inconsistent results. This study proposes a machine learning-based approach for physical fitness evaluation through motion detection using pose estimation and exercise classification models. A quantitative method was employed to train and evaluate models for four exercise types: push-ups, sit-ups, pull-ups, and chinning. Each model was trained separately and assessed using accuracy, precision, recall, and F1-score metrics, achieving accuracies of 97.50% for push-ups, 97.67% for sit-ups, 97.00% for pull-ups, and 98.50% for chinning. The maximum error margin compared to manual counting was 2.48%. System-generated outputs were validated against manual observations using standard evaluation matrices. These findings indicate that machine learning can offer a reliable, consistent, and automated solution for physical fitness assessment, with the potential to enhance training programs, support remote fitness monitoring, and reduce human error in performance evaluation.
Portable oceanic solutions for enhanced IoT-based desalination and salt extraction (POSEIDON) Randi Agustio; Onky Prilianda Putra; Dananjaya Ariateja; Refino Maulana Hansbullah Subarkah; H. A Danang Rimbawa
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The clean water crisis remains a significant challenge in many remote areas, particularly on small islands in Indonesia where freshwater resources are limited. Desalination technology offers a promising solution; however, conventional methods often face obstacles such as high energy consumption, costly operations, and limited real-time water quality monitoring. This study aims to design and evaluate a distillation-based desalination device integrated with Internet of Things (IoT) technology, called POSEIDON. The system utilizes solar energy and heating elements to support the distillation process and is equipped with pH, TDS, ultrasonic, and water level sensors connected to the Blynk application for real-time monitoring and alert notifications. Testing was conducted over 10 hours under both daytime and nighttime conditions. Results show that the distilled water had pH values ranging from 7.01 to 7.51 and PPM values from 798 to 588.38. One-way ANOVA indicated no statistically significant variation (p > 0.05), demonstrating consistent system performance. The average volume of fresh water produced was 0.403 liters from 0.7 liters of seawater, with an average salt yield of 23.1 grams. POSEIDON exhibits good energy efficiency and portability, and it can operate at night. Nevertheless, improvements are needed in production capacity and water quality. Overall, POSEIDON presents a viable and sustainable solution to meet clean water needs in remote, water-scarce regions.
Design and development of an IoT-based archive room security system integrating RFID and fingerprint authentication for military document protection Tidar, R Haryo; Madramsyah, Adam; Rimbawa, H.A Danang; Sembali, Tryas Putranto
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.440

Abstract

The objective of this research is to design and implement a secure, IoT-based dual-authentication system for protecting classified military archive rooms, in response to the growing urgency of safeguarding sensitive documents against real threats such as espionage, unauthorized access, and data tampering. Military archives store critical information essential for national defense operations, yet many facilities continue to rely on outdated physical security systems vulnerable to intrusion and lacking auditability. This research presents the design and implementation of a dual-authentication archive security system based on Internet of Things (IoT), integrating Radio Frequency Identification (RFID) and fingerprint biometrics. The system is developed using the Waterfall model, involving sequential stages of requirement analysis, system design, implementation, testing, and evaluation. The NodeMCU ESP32 microcontroller serves as the central controller, enabling real-time data transmission via Wi-Fi and notification delivery through the Telegram API. The RFID module performs initial identification, while the fingerprint sensor confirms biometric authentication. A solenoid lock mechanism provides physical access control, activated only upon successful dual verification. System testing under simulated military archive conditions yielded an average response time of 4.59 seconds and an authentication accuracy of 90.6%. Additionally, the real-time notification feature enhanced situational awareness by informing administrators of all access events—both valid and unauthorized. The results indicate that combining RFID and fingerprint authentication significantly improves system security, auditability, and operational efficiency compared to single-factor or conventional methods. This system demonstrates the potential for scalable, adaptable application in high-security institutional environments. Future development may include integration of backup power supplies, encrypted communication protocols, and expansion toward a more comprehensive digital security architecture. This research contributes to the advancement of smart security systems in military infrastructure, promoting proactive threat mitigation and enhanced document protection.
Implementasi Teknologi Mediapipe Menggunakan Metode CNN Berbasis Website Untuk Pengamanan VVIP Dalam Mobil M. Ilham AlFatrah; Hery Sudaryanto; H. A. Danang Rimbawa
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 3 (2025): July
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penelitian ini bertujuan mengembangkan sistem deteksi gestur tangan berbasis MediaPipe dan Convolutional Neural Network (CNN) guna meningkatkan efektivitas pengamanan VVIP. Mengingat ancaman modern yang semakin kompleks, sistem ini dirancang untuk mendeteksi gestur darurat secara real-time dan memungkinkan respons cepat. Metodologi yang digunakan meliputi pengumpulan dataset gestur tangan, anotasi data menggunakan MediaPipe, dan pelatihan model CNN di Google Colab. Kinerja model dievaluasi dengan metrik akurasi, presisi, recall, dan F1-score. Pengujian juga dilakukan dalam berbagai kondisi, seperti pencahayaan rendah dan gerakan cepat, untuk menilai ketangguhan sistem di dunia nyata. Hasilnya, sistem ini berhasil mendeteksi gestur tangan darurat dengan akurasi tinggi dan kecepatan kurang dari satu detik. Kinerja optimal, dengan akurasi mendekati 100%, tercapai pada kondisi pencahayaan yang baik. Meskipun akurasi sedikit menurun pada kondisi ekstrem, integrasi sistem pada platform website memungkinkan pengawasan dan pengambilan keputusan cepat di pusat komando. Penelitian ini membuktikan bahwa kombinasi MediaPipe dan CNN adalah solusi inovatif, namun optimasi lebih lanjut tetap dibutuhkan.
Analysis and design of an inset-feed microstrip antenna for a LEO satellite IoT ground station at 921 MHz Taqwa, Rangga; Rimbawa, H.A. Danang; Miptahudin, Apip; Hasibuan, Bayu Nuar Khadapi; Sastradinata, Aria Kusumah; Bangun, Abbas Madani
Jurnal Mandiri IT Vol. 14 No. 2 (2025): Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i2.464

Abstract

The evolution of the Internet of Things (IoT) demands global connectivity that terrestrial networks alone cannot provide1. Low Earth Orbit (LEO) satellites equipped with Long Range (LoRa) communication technology offer a promising solution to bridge this connectivity gap2. This paper presents a specific case study calculation for a LoRa-based IoT satellite mission, defining the system's operational constraints based on selected hardware3. This analysis is framed by the RFM95W LoRa transceiver for the ground station and the Satlab Polaris receiver for the satellite4. The datasheet specifications of these components establish the critical link parameters that dictate performance: a maximum Transmit Power (Pt) ) of 20 dBm from the RFM95W 5and a Receiver Sensitivity threshold of -130 dBm for the Satlab Polaris6. The objectives are: (1) to conduct a comprehensive link budget analysis to validate the communication viability between a LEO satellite and a ground station 77, and (2) to design and predict the performance of an inset-feed microstrip antenna operating in the 920-925 MHz Indonesian LoRa frequency band using an FR-4 substrate. The detailed link budget analysis, performed for an uplink to a 500 km orbit 9, reveals that these specific parameters create a stringent performance requirement: while a reliable link margin of $+7.8 \text{ dB}$ is achieved at a 90°  elevation (best case) 10101010, the system reaches its theoretical critical threshold (0.0 dB margin) at 19.1° and enters link failure with a -2.8 dB margin at the target 10°  elevation. This failure is directly linked to the preliminary simulation of the initial antenna design, which shows a suboptimal return loss (S11) of -9.41 dB. This paper concludes that the system's target for low-elevation communication has not been met. The performance gap, defined by the hardware constraints, confirms that the initial antenna design is insufficient15. Therefore, systematic optimization of the antenna design is identified as the crucial next step to achieve a positive link margin at the 10° target elevation and ensure a robust communication link across all operational scenarios.
Improving Large Language Model’s Ability to Find the Words Relationship Alam, Sirojul; Abdul Jabar, Jaka; Abdurrachman, Fauzi; Suharjo, Bambang; Rimbawa, H.A Danang
Jurnal Bumigora Information Technology (BITe) Vol. 6 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v6i2.4127

Abstract

Background: It is still possible to enhance the capabilities of popular and widely used large language models (LLMs) such as Generative Pre-trained Transformer (GPT). Using the Retrieval-Augmented Generation (RAG) architecture is one method of achieving enhancement. This architectural approach incorporates outside data into the model to improve LLM capabilities. Objective: The aim of this research is to prove that the RAG can help LLMs respond with greater precision and rationale. Method: The method used in this work is utilizing Huggingface Application Programming Interface (API) for word embedding, store and find the relationship of the words. Result: The results show how well RAG performs, as the attractively rendered graph makes clear. The knowledge that has been obtained is logical and understandable, such as the word Logistic Regression that related to accuracy, F1 score, and defined as a simple and the best model compared to Naïve Bayes and Support Vector Machine (SVM) model. Conclusion: The conclusion is RAG helps LLMs to improve its capability well.
Co-Authors Abdillah, Abdillah Imam Julianto Abdul Jabar, Jaka Abdurrachman, Fauzi Alam, Sirojul AlFatrah, M. Ilham Amalia, Nuke Ananda, Dea Dwi Andri Purwoko Arghanie, Muhammad Abditya Ari Susanti Atika Mima Amalin Atturoybi, Abdurrosyid Aulia Khamas H Bambang Suhardjo Bambang Suharjo Bambang Suharjo Bangun, Abbas Madani Bisyron Wahyudi Bisyron Wahyudi Budi Rahardjo Budi Raharjo Chadafa Zulti Noorta Dadan Shavkat Riswantoro Dananjaya Ariateja Danny Setyowati Fadhil Muhammad Hadini Faidin, Firman Gilang Prakoso Harry Pratomo Bagaskoro Hasibuan, Bayu Nuar Khadapi Heikhmakhtiar, Aulia Khamas Heri Azhari Noor Hery Sudaryanto Hery Sudaryanto Hutapea, Herwin Melyanus I Made Wiryana I Made Wiryana Kencana, Lisdi Inu Legowo, Danang Luqman, Fathan M. Fachrurrozy M. Ilham AlFatrah M. Ilham AlFatrah Madramsyah, Adam Maya Ayu Riestiyowati Miptahudin, Apip Muhamad Zein Satria Ni Putu Ayu Astriyani Noorta, Chadafa Zulti Nurrahman, Muhammad Irsyaad Onky Prilianda Putra Passu Beta, Arga Husein Puri Ratna Larasati Puringgar Prasadha Putra, Rayasa Rais, M. Fazil Randi Agustio Ra’idah Naufaliana Dewi Refino Maulana Hansbullah Subarkah Renoult, Muhammad Rey Richardus Eko Indrajit Riswantoro, Dadan Shavkat Ruby Alamsyah Rustam, Muh Zul Azhri Saputro, J.W. Saragih, Gabriel Winandika Sastradinata, Aria Kusumah Sembali, Tryas Putranto Suhardiningsih, A.V. Sri Suharjo, Bambang Sunarta Sunarta Supriyadi, Devi Tangang Qisthina Handayani Zatadini Taqwa, Rangga Tidar, R Haryo TUTUN JUHANA Uvi Desi Fatmawati Wahyudi, Bisyron Wibisono, Nugroho Yosef Prihanto Yudistira Asnar